{
 "cells": [
  {
   "cell_type": "code",
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2025-01-09T11:38:40.080727Z",
     "start_time": "2025-01-09T11:38:39.305427Z"
    }
   },
   "source": [
    "# 导入库\n",
    "from sklearn.feature_extraction import DictVectorizer\n",
    "from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\n",
    "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
    "from sklearn.feature_selection import VarianceThreshold\n",
    "from sklearn.decomposition import PCA\n",
    "import jieba\n",
    "import numpy as np\n",
    "from sklearn.impute import SimpleImputer"
   ],
   "outputs": [],
   "execution_count": 1
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "# 1 特征中含有字符串时（当成类别）的特征抽取",
   "id": "27b119e125326719"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-09T11:50:31.261486Z",
     "start_time": "2025-01-09T11:50:31.256487Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def dictvec():\n",
    "    \"\"\"\n",
    "    字典数据特征抽取\n",
    "    \"\"\"\n",
    "    # 实例化\n",
    "    # spares=True表示输出每个不为0位置的坐标（三元组），稀疏矩阵节省存储空间\n",
    "    # Vectorizer中文含义：矢量器，将字典数据转换为向量\n",
    "    dict1 = DictVectorizer(sparse=False)\n",
    "\n",
    "    #每个样本都是一个字典，有三个样本\n",
    "    # 调用fit_transform: 训练并转换数据\n",
    "    # 输入数据格式：list of dicts\n",
    "    data = dict1.fit_transform([{'city': '北京', 'temperature': 100},\n",
    "                                {'city': '上海', 'temperature': 60},\n",
    "                                {'city': '深圳', 'temperature': 30}])\n",
    "\n",
    "    # 字典中类别数据转换为特征\n",
    "    # 独热编码: 将类别数据转换为0-1向量，每个位置对应一个类别\n",
    "    print(\"独热编码结果：\\n\", data)\n",
    "\n",
    "    # 调用get_feature_names_out获取类别特征名称\n",
    "    print(\"类别特征名称：\\n\", dict1.get_feature_names_out())\n",
    "\n",
    "    # 特征逆转\n",
    "    # 调用inverse_transform将特征值转换为字典\n",
    "    print(\"特征逆转结果：\\n\", dict1.inverse_transform(data))\n",
    "\n",
    "    return None\n",
    "\n",
    "\n",
    "dictvec()"
   ],
   "id": "3f7b19553e4f2f17",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "独热编码结果：\n",
      " [[  0.   1.   0. 100.]\n",
      " [  1.   0.   0.  60.]\n",
      " [  0.   0.   1.  30.]]\n",
      "类别特征名称：\n",
      " ['city=上海' 'city=北京' 'city=深圳' 'temperature']\n",
      "特征逆转结果：\n",
      " [{'city=北京': np.float64(1.0), 'temperature': np.float64(100.0)}, {'city=上海': np.float64(1.0), 'temperature': np.float64(60.0)}, {'city=深圳': np.float64(1.0), 'temperature': np.float64(30.0)}]\n"
     ]
    }
   ],
   "execution_count": 12
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "# 2 英文文本变为数值类型",
   "id": "bbf0bbbe31362636"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-09T12:00:00.626393Z",
     "start_time": "2025-01-09T12:00:00.621414Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def couvec():\n",
    "    \"\"\"\n",
    "    词频统计特征抽取\n",
    "    \"\"\"\n",
    "    # 实例化CountVectorizer\n",
    "    # max_df: 词频阈值，超过该阈值的词将被丢弃,整数或浮点数\n",
    "    # min_df: 词频阈值，低于该阈值的词将被丢弃，整数或浮点数\n",
    "    # 整数：某个词的出现的次数\n",
    "    # 小数(0-1之间的）：某个词的出现的次数／所有文档数量\n",
    "    # 默认去除单个字母的单词，默认这个词对整个样本没有影响，认为没有意义\n",
    "\n",
    "    vector = CountVectorizer(min_df=2)\n",
    "\n",
    "    # 调用fit_transform输入并转换数据\n",
    "\n",
    "    res = vector.fit_transform(\n",
    "        [\"life is  short,i like python life\",\n",
    "         \"life is too long,i dislike python\",\n",
    "         \"life is short\"])\n",
    "\n",
    "    # 调用get_feature_names获取特征名称\n",
    "    print(\"特征名称：\\n\", vector.get_feature_names_out())\n",
    "\n",
    "    # res作用：词频统计结果，类型是csr_matrix\n",
    "    print(\"词频统计结果：\\n\", res)\n",
    "    print(\"词频统计结果类型：\", type(res))\n",
    "\n",
    "    # toarray()将矩阵转换为数组\n",
    "    print(\"转化为数组：\", res.toarray())\n",
    "\n",
    "    # 调用inverse_transform将特征值转换为文本\n",
    "    print(\"特征逆转结果：\\n\", vector.inverse_transform(res))\n",
    "\n",
    "\n",
    "couvec()"
   ],
   "id": "2b41b5332498747",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "特征名称：\n",
      " ['is' 'life' 'python' 'short']\n",
      "词频统计结果：\n",
      " <Compressed Sparse Row sparse matrix of dtype 'int64'\n",
      "\twith 10 stored elements and shape (3, 4)>\n",
      "  Coords\tValues\n",
      "  (0, 1)\t2\n",
      "  (0, 0)\t1\n",
      "  (0, 3)\t1\n",
      "  (0, 2)\t1\n",
      "  (1, 1)\t1\n",
      "  (1, 0)\t1\n",
      "  (1, 2)\t1\n",
      "  (2, 1)\t1\n",
      "  (2, 0)\t1\n",
      "  (2, 3)\t1\n",
      "词频统计结果类型： <class 'scipy.sparse._csr.csr_matrix'>\n",
      "转化为数组： [[1 2 1 1]\n",
      " [1 1 1 0]\n",
      " [1 1 0 1]]\n",
      "特征逆转结果：\n",
      " [array(['life', 'is', 'short', 'python'], dtype='<U6'), array(['life', 'is', 'python'], dtype='<U6'), array(['life', 'is', 'short'], dtype='<U6')]\n"
     ]
    }
   ],
   "execution_count": 17
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "# 汉字文本数值化，对于汉字不能用空格来分割",
   "id": "bd6d6be01b73d8ef"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-09T12:18:57.896154Z",
     "start_time": "2025-01-09T12:18:57.891805Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def countvec():\n",
    "    \"\"\"\n",
    "    对文本进行特征化，单个汉字单个字母不统计，单个汉字没有意义\n",
    "    \"\"\"\n",
    "    # 实例化CountVectorizer\n",
    "    cv = CountVectorizer()\n",
    "\n",
    "    data = cv.fit_transform([\"人生苦短，我喜欢 python python\", \"人生漫长，不用 python\"])\n",
    "\n",
    "    # 调用get_feature_names获取特征名称\n",
    "    print(\"特征名称：\\n\", cv.get_feature_names_out())\n",
    "\n",
    "    # data作用：词频统计结果，稀疏矩阵，只记录非零元素的坐标\n",
    "    print(\"词频统计结果：\\n\", data)\n",
    "\n",
    "    # .toarray()将矩阵转换为数组\n",
    "    print(\"转化为数组：\\n\", data.toarray())\n",
    "\n",
    "    # 调用inverse_transform将特征值转换为文本\n",
    "    print(\"特征逆转结果：\\n\", cv.inverse_transform(data))\n",
    "\n",
    "\n",
    "countvec()"
   ],
   "id": "19690bc266636091",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "特征名称：\n",
      " ['python' '不用' '人生漫长' '人生苦短' '我喜欢']\n",
      "词频统计结果：\n",
      " <Compressed Sparse Row sparse matrix of dtype 'int64'\n",
      "\twith 6 stored elements and shape (2, 5)>\n",
      "  Coords\tValues\n",
      "  (0, 3)\t1\n",
      "  (0, 4)\t1\n",
      "  (0, 0)\t2\n",
      "  (1, 0)\t1\n",
      "  (1, 2)\t1\n",
      "  (1, 1)\t1\n",
      "转化为数组：\n",
      " [[2 0 0 1 1]\n",
      " [1 1 1 0 0]]\n",
      "特征逆转结果：\n",
      " [array(['人生苦短', '我喜欢', 'python'], dtype='<U6'), array(['python', '人生漫长', '不用'], dtype='<U6')]\n"
     ]
    }
   ],
   "execution_count": 21
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "# 对中文分词",
   "id": "567fa2019db447c5"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-09T12:26:25.306826Z",
     "start_time": "2025-01-09T12:26:25.299966Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def cutword():\n",
    "    \"\"\"\n",
    "    对中文文本进行分词，使用jieba分词\n",
    "    \"\"\"\n",
    "    # 数据\n",
    "    con1 = jieba.cut(\"今天很残酷，明天更残酷，后天很美好，但绝对大部分是死在明天晚上，所以每个人不要放弃今天。\")\n",
    "\n",
    "    con2 = jieba.cut(\"我们看到的从很远星系来的光是在几百万年之前发出的，这样当我们看到宇宙时，我们是在看它的过去。\")\n",
    "\n",
    "    con3 = jieba.cut(\n",
    "        \"如果只用一种方式了解某样事物，你就不会真正了解它。了解事物真正含义的秘密取决于如何将其与我们所了解的事物相联系。\")\n",
    "\n",
    "    # cut返回生成器，需要转化为list\n",
    "    print(\"分词类型：\\n\", type(con1))\n",
    "\n",
    "    # 生成器转化为list\n",
    "    content1 = list(con1)\n",
    "    content2 = list(con2)\n",
    "    content3 = list(con3)\n",
    "    # 打印分词结果\n",
    "    print(\"分词结果：\\n\")\n",
    "    print(content1)\n",
    "    print(content2)\n",
    "    print(content3)\n",
    "\n",
    "    # 把列表转化为字符串，用空格分隔\n",
    "    c1 = ' '.join(content1)\n",
    "    c2 = ' '.join(content2)\n",
    "    c3 = ' '.join(content3)\n",
    "\n",
    "    return c1, c2, c3\n",
    "\n",
    "\n",
    "def hanzivec():\n",
    "    \"\"\"\n",
    "    中文特征值化\n",
    "    \"\"\"\n",
    "    # jieba分词\n",
    "    c1, c2, c3 = cutword()\n",
    "\n",
    "    print(\"分词结果：\\n\", c1, c2, c3)\n",
    "\n",
    "    # 实例化CountVectorizer\n",
    "    cv = CountVectorizer()\n",
    "\n",
    "    # 调用fit_transform输入并转换数据, 输入数据格式：list of strings\n",
    "    data = cv.fit_transform([c1, c2, c3])\n",
    "\n",
    "    # 调用get_feature_names获取特征名称\n",
    "    print(\"特征名称：\\n\", cv.get_feature_names_out())\n",
    "\n",
    "    # 转化为数组\n",
    "    print(\"词频统计结果：\\n\", data.toarray())\n",
    "\n",
    "    # 调用inverse_transform将特征值转换为文本\n",
    "    print(\"特征逆转结果：\\n\", cv.inverse_transform(data))\n",
    "\n",
    "    return None\n",
    "\n",
    "\n",
    "hanzivec()"
   ],
   "id": "916fb8b2cb02bc1b",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "分词类型：\n",
      " <class 'generator'>\n",
      "分词结果：\n",
      "\n",
      "['今天', '很', '残酷', '，', '明天', '更', '残酷', '，', '后天', '很', '美好', '，', '但', '绝对', '大部分', '是', '死', '在', '明天', '晚上', '，', '所以', '每个', '人', '不要', '放弃', '今天', '。']\n",
      "['我们', '看到', '的', '从', '很', '远', '星系', '来', '的', '光是在', '几百万年', '之前', '发出', '的', '，', '这样', '当', '我们', '看到', '宇宙', '时', '，', '我们', '是', '在', '看', '它', '的', '过去', '。']\n",
      "['如果', '只用', '一种', '方式', '了解', '某样', '事物', '，', '你', '就', '不会', '真正', '了解', '它', '。', '了解', '事物', '真正', '含义', '的', '秘密', '取决于', '如何', '将', '其', '与', '我们', '所', '了解', '的', '事物', '相', '联系', '。']\n",
      "分词结果：\n",
      " 今天 很 残酷 ， 明天 更 残酷 ， 后天 很 美好 ， 但 绝对 大部分 是 死 在 明天 晚上 ， 所以 每个 人 不要 放弃 今天 。 我们 看到 的 从 很 远 星系 来 的 光是在 几百万年 之前 发出 的 ， 这样 当 我们 看到 宇宙 时 ， 我们 是 在 看 它 的 过去 。 如果 只用 一种 方式 了解 某样 事物 ， 你 就 不会 真正 了解 它 。 了解 事物 真正 含义 的 秘密 取决于 如何 将 其 与 我们 所 了解 的 事物 相 联系 。\n",
      "特征名称：\n",
      " ['一种' '不会' '不要' '之前' '了解' '事物' '今天' '光是在' '几百万年' '发出' '取决于' '只用' '后天' '含义'\n",
      " '大部分' '如何' '如果' '宇宙' '我们' '所以' '放弃' '方式' '明天' '星系' '晚上' '某样' '残酷' '每个'\n",
      " '看到' '真正' '秘密' '绝对' '美好' '联系' '过去' '这样']\n",
      "词频统计结果：\n",
      " [[0 0 1 0 0 0 2 0 0 0 0 0 1 0 1 0 0 0 0 1 1 0 2 0 1 0 2 1 0 0 0 1 1 0 0 0]\n",
      " [0 0 0 1 0 0 0 1 1 1 0 0 0 0 0 0 0 1 3 0 0 0 0 1 0 0 0 0 2 0 0 0 0 0 1 1]\n",
      " [1 1 0 0 4 3 0 0 0 0 1 1 0 1 0 1 1 0 1 0 0 1 0 0 0 1 0 0 0 2 1 0 0 1 0 0]]\n",
      "特征逆转结果：\n",
      " [array(['今天', '残酷', '明天', '后天', '美好', '绝对', '大部分', '晚上', '所以', '每个', '不要',\n",
      "       '放弃'], dtype='<U4'), array(['我们', '看到', '星系', '光是在', '几百万年', '之前', '发出', '这样', '宇宙', '过去'],\n",
      "      dtype='<U4'), array(['我们', '如果', '只用', '一种', '方式', '了解', '某样', '事物', '不会', '真正', '含义',\n",
      "       '秘密', '取决于', '如何', '联系'], dtype='<U4')]\n"
     ]
    }
   ],
   "execution_count": 23
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "# TF-IDF    ",
   "id": "76431469a207acd9"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-09T12:33:05.994368Z",
     "start_time": "2025-01-09T12:33:05.984882Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def tfidfvec():\n",
    "    \"\"\"\n",
    "    TF-IDF特征抽取\n",
    "    中文特征值化，计算TF-IDF值，权重高的词语重要性高，权重低的词语重要性低，权重高的词语在文档中出现次数多，权重低的词语在文档中出现次数少\n",
    "    \"\"\"\n",
    "    # {'l1','l2'},默认是{'l2'}，表示正则化方式，l1正则化，l2正则化\n",
    "    # 'l2'：向量元素的平方和为 1。当应用 l2 范数时，两个向量之间的余弦相似度是它们的点积。\n",
    "    # 'l1'：向量元素的绝对值之和为 1。当应用 l1 范数时，两个向量之间的 Manhattan 距离是它们的绝对值之和。\n",
    "    # smooth_idf：布尔值,默认是True，表示是否平滑 idf 值。\n",
    "    # 通过在文档频率上加一来平滑 idf 权重，就好像看到一个额外的文档包含集合中的每个术语恰好一次。防止零分裂。\n",
    "    # 比如训练集中有某个词，测试集中没有，就是生僻词，就会造成n(x)分母为零，log(n/n(x)),从而出现零分裂\n",
    "\n",
    "    # 实例化TfidfVectorizer\n",
    "    tf = TfidfVectorizer(smooth_idf=True)\n",
    "\n",
    "    c1, c2, c3 = cutword()\n",
    "\n",
    "    data = tf.fit_transform([c1, c2, c3])\n",
    "\n",
    "    # 调用get_feature_names获取特征名称\n",
    "    print(\"特征名称：\\n\", tf.get_feature_names_out())\n",
    "\n",
    "    # 转化为数组\n",
    "    print(\"词频统计结果：\\n\", data.toarray())\n",
    "\n",
    "    # 调用inverse_transform将特征值转换为文本\n",
    "    print(\"特征逆转结果：\\n\", tf.inverse_transform(data))\n",
    "\n",
    "    return None\n",
    "\n",
    "\n",
    "tfidfvec()"
   ],
   "id": "f3d079a929525ae5",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "分词类型：\n",
      " <class 'generator'>\n",
      "分词结果：\n",
      "\n",
      "['今天', '很', '残酷', '，', '明天', '更', '残酷', '，', '后天', '很', '美好', '，', '但', '绝对', '大部分', '是', '死', '在', '明天', '晚上', '，', '所以', '每个', '人', '不要', '放弃', '今天', '。']\n",
      "['我们', '看到', '的', '从', '很', '远', '星系', '来', '的', '光是在', '几百万年', '之前', '发出', '的', '，', '这样', '当', '我们', '看到', '宇宙', '时', '，', '我们', '是', '在', '看', '它', '的', '过去', '。']\n",
      "['如果', '只用', '一种', '方式', '了解', '某样', '事物', '，', '你', '就', '不会', '真正', '了解', '它', '。', '了解', '事物', '真正', '含义', '的', '秘密', '取决于', '如何', '将', '其', '与', '我们', '所', '了解', '的', '事物', '相', '联系', '。']\n",
      "特征名称：\n",
      " ['一种' '不会' '不要' '之前' '了解' '事物' '今天' '光是在' '几百万年' '发出' '取决于' '只用' '后天' '含义'\n",
      " '大部分' '如何' '如果' '宇宙' '我们' '所以' '放弃' '方式' '明天' '星系' '晚上' '某样' '残酷' '每个'\n",
      " '看到' '真正' '秘密' '绝对' '美好' '联系' '过去' '这样']\n",
      "词频统计结果：\n",
      " [[0.         0.         0.21821789 0.         0.         0.\n",
      "  0.43643578 0.         0.         0.         0.         0.\n",
      "  0.21821789 0.         0.21821789 0.         0.         0.\n",
      "  0.         0.21821789 0.21821789 0.         0.43643578 0.\n",
      "  0.21821789 0.         0.43643578 0.21821789 0.         0.\n",
      "  0.         0.21821789 0.21821789 0.         0.         0.        ]\n",
      " [0.         0.         0.         0.2410822  0.         0.\n",
      "  0.         0.2410822  0.2410822  0.2410822  0.         0.\n",
      "  0.         0.         0.         0.         0.         0.2410822\n",
      "  0.55004769 0.         0.         0.         0.         0.2410822\n",
      "  0.         0.         0.         0.         0.48216441 0.\n",
      "  0.         0.         0.         0.         0.2410822  0.2410822 ]\n",
      " [0.15698297 0.15698297 0.         0.         0.62793188 0.47094891\n",
      "  0.         0.         0.         0.         0.15698297 0.15698297\n",
      "  0.         0.15698297 0.         0.15698297 0.15698297 0.\n",
      "  0.1193896  0.         0.         0.15698297 0.         0.\n",
      "  0.         0.15698297 0.         0.         0.         0.31396594\n",
      "  0.15698297 0.         0.         0.15698297 0.         0.        ]]\n",
      "特征逆转结果：\n",
      " [array(['今天', '残酷', '明天', '后天', '美好', '绝对', '大部分', '晚上', '所以', '每个', '不要',\n",
      "       '放弃'], dtype='<U4'), array(['我们', '看到', '星系', '光是在', '几百万年', '之前', '发出', '这样', '宇宙', '过去'],\n",
      "      dtype='<U4'), array(['我们', '如果', '只用', '一种', '方式', '了解', '某样', '事物', '不会', '真正', '含义',\n",
      "       '秘密', '取决于', '如何', '联系'], dtype='<U4')]\n"
     ]
    }
   ],
   "execution_count": 24
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "# 特征处理，不同特征拉到同一量纲\n",
    "### 量纲：特征的大小，单位，范围"
   ],
   "id": "127d7bc7c4bf8b39"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-09T12:37:51.101896Z",
     "start_time": "2025-01-09T12:37:51.096895Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def mm():\n",
    "    \"\"\"\n",
    "    归一化，Max-Min归一化\n",
    "    \"\"\"\n",
    "    # 缺点：容易受极值的影响\n",
    "\n",
    "    # 实例化MinMaxScaler\n",
    "    # feature_range=(0,1)表示归一化后的值在0-1之间,feature_range:元组，表示归一化后的值的范围\n",
    "    mm = MinMaxScaler(feature_range=(0, 1))\n",
    "\n",
    "    # 输入数据格式：list of arrays\n",
    "    # fit_transform: 训练并转换数据\n",
    "    data = mm.fit_transform([[90, 2, 10, 40], [60, 4, 15, 45], [75, 3, 13, 46]])\n",
    "    print(\"训练数据：\\n\", data)\n",
    "\n",
    "    # 调用transform将数据转换为0-1之间\n",
    "    # .transform: 转换数据\n",
    "    print(\"测试数据归一化结果：\\n\", mm.transform([[1, 2, 3, 4], [6, 5, 8, 7]]))\n",
    "\n",
    "    return None\n",
    "\n",
    "\n",
    "mm()"
   ],
   "id": "4066fc830aeeaf4a",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "训练数据：\n",
      " [[1.         0.         0.         0.        ]\n",
      " [0.         1.         1.         0.83333333]\n",
      " [0.5        0.5        0.6        1.        ]]\n",
      "测试数据归一化结果：\n",
      " [[-1.96666667  0.         -1.4        -6.        ]\n",
      " [-1.8         1.5        -0.4        -5.5       ]]\n"
     ]
    }
   ],
   "execution_count": 27
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-09T12:58:57.649709Z",
     "start_time": "2025-01-09T12:58:57.645783Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def stand():\n",
    "    \"\"\"\n",
    "    标准化，StandardScaler,将数据标准化到平均值为0，方差为1\n",
    "    不是标准正态分布，是均值为0，方差为1的均匀分布\n",
    "    \"\"\"\n",
    "\n",
    "    # 实例化StandardScaler\n",
    "    std = StandardScaler()\n",
    "\n",
    "    data = std.fit_transform([[1., -1., 3.],\n",
    "                              [2., 4., 2.],\n",
    "                              [4., 6., -1.]])\n",
    "\n",
    "    print(\"训练数据：\\n\", data)\n",
    "\n",
    "    # .mean_：均值\n",
    "    print(std.mean_)\n",
    "    # .var_：方差\n",
    "    print(std.var_)\n",
    "\n",
    "    # .n_samples_seen_: 样本数\n",
    "    print(std.n_samples_seen_)\n",
    "\n",
    "    return data\n",
    "\n",
    "\n",
    "# 类型是numpy.ndarray\n",
    "data = stand()"
   ],
   "id": "beff4416c397c6b3",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "训练数据：\n",
      " [[-1.06904497 -1.35873244  0.98058068]\n",
      " [-0.26726124  0.33968311  0.39223227]\n",
      " [ 1.33630621  1.01904933 -1.37281295]]\n",
      "[2.33333333 3.         1.33333333]\n",
      "[1.55555556 8.66666667 2.88888889]\n",
      "3\n"
     ]
    }
   ],
   "execution_count": 29
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## transform和fit_transform不同是，transform用于测试集，而且不会重新找最小值和最大值,不会重新计算均值方差",
   "id": "91ef03942b61352"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "# 缺失值处理",
   "id": "ac6698d63d467a3a"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-09T13:04:43.092025Z",
     "start_time": "2025-01-09T13:04:43.088128Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def im():\n",
    "    \"\"\"\n",
    "    缺失值处理，SimpleImputer\n",
    "    \"\"\"\n",
    "    # 缺失值填补，针对删除可用pd，np\n",
    "    # 缺失值只能是Nan，如果是？或者其他字符，用replace转换为Nan\n",
    "    # mean：用均值填补, median：用中位数填补, most_frequent：用众数填补, constant：用常数填补\n",
    "\n",
    "    # 实例化SimpleImputer\n",
    "    # missing_values：缺失值，默认是np.nan\n",
    "    # strategy：填补策略，默认是mean\n",
    "    im = SimpleImputer(missing_values=np.nan, strategy='mean')\n",
    "\n",
    "    data = im.fit_transform([[1, 2], [np.nan, 3], [7, 6], [3, 2]])\n",
    "\n",
    "    print(\"clean 数据：\\n\", data)\n",
    "\n",
    "    return None\n",
    "\n",
    "\n",
    "im()"
   ],
   "id": "bcaa476ee3be623b",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "clean 数据：\n",
      " [[1.         2.        ]\n",
      " [3.66666667 3.        ]\n",
      " [7.         6.        ]\n",
      " [3.         2.        ]]\n"
     ]
    }
   ],
   "execution_count": 32
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "# 降维\n",
    "### 降维就是特征数变少，提高模型训练速度"
   ],
   "id": "67950406f775fa7b"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-09T13:07:11.112719Z",
     "start_time": "2025-01-09T13:07:11.108661Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def var():\n",
    "    \"\"\"\n",
    "    特征选择，VarianceThreshold\n",
    "    删除低方差的特征\n",
    "    \"\"\"\n",
    "\n",
    "    # 实例化VarianceThreshold\n",
    "    # threshold：方差阈值，默认是0\n",
    "    var = VarianceThreshold(threshold=0.1)\n",
    "\n",
    "    data = var.fit_transform([[0, 2, 0, 3],\n",
    "                              [0, 1, 4, 3],\n",
    "                              [0, 1, 1, 3]])\n",
    "\n",
    "    print(\"处理后数据：\\n\", data)\n",
    "\n",
    "    # 调用get_support获取选择的特征\n",
    "    print(\"选择的特征：\\n\", var.get_support(True))\n",
    "\n",
    "    return None\n",
    "\n",
    "\n",
    "var()"
   ],
   "id": "6cc1eae4073e937e",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "处理后数据：\n",
      " [[2 0]\n",
      " [1 4]\n",
      " [1 1]]\n",
      "选择的特征：\n",
      " [1 2]\n"
     ]
    }
   ],
   "execution_count": 35
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-09T13:09:20.796102Z",
     "start_time": "2025-01-09T13:09:20.791226Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def pca():\n",
    "    \"\"\"\n",
    "    主成分分析，PCA\n",
    "    \"\"\"\n",
    "\n",
    "    # 实例化PCA\n",
    "    # n_ components:小数 0~1 90% 业界选择 90~95%\n",
    "    # 当n_components的值为0到1之间的浮点数时，表示我们希望保留的主成分解释的方差比例。方差比例是指得到输出的每一列的方差值和除以原有数据方差之和。\n",
    "    # 具体而言，n_components=0.9表示我们希望选择足够的主成分，以使它们解释数据方差的90%。\n",
    "    # n_components如果是整数   减少到的特征数量\n",
    "\n",
    "    #原始数据\n",
    "    original_value = np.array([[2, 8, 4, 5],\n",
    "                               [6, 3, 0, 8],\n",
    "                               [5, 4, 9, 1]])\n",
    "\n",
    "    # 实例化PCA\n",
    "    pca = PCA(n_components=0.99)\n",
    "\n",
    "    data = pca.fit_transform(original_value)\n",
    "\n",
    "    print(\"降维后数据：\\n\", data)\n",
    "\n",
    "    # 调用explained_variance_ratio_获取特征方差比例\n",
    "    print(\"特征方差比例：\\n\", pca.explained_variance_ratio_)\n",
    "\n",
    "    # 计算data的方差占总方差的比例\n",
    "    print(\"方差占总方差的比例：\", pca.explained_variance_ratio_.sum())\n",
    "\n",
    "    return None\n",
    "\n",
    "\n",
    "pca()"
   ],
   "id": "e383f7776768aac",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "降维后数据：\n",
      " [[-1.28620952e-15  3.82970843e+00]\n",
      " [-5.74456265e+00 -1.91485422e+00]\n",
      " [ 5.74456265e+00 -1.91485422e+00]]\n",
      "特征方差比例：\n",
      " [0.75 0.25]\n",
      "方差占总方差的比例： 1.0\n"
     ]
    }
   ],
   "execution_count": 37
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-09T13:09:35.886694Z",
     "start_time": "2025-01-09T13:09:35.604563Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from matplotlib import pyplot as plt\n",
    "\n",
    "x = np.random.rand(10000)  #每个的概率\n",
    "t = np.arange(len(x))\n",
    "plt.plot(t, x, 'g.', label=\"Uniform Distribution\")\n",
    "plt.legend(loc=\"upper left\")\n",
    "plt.grid()\n",
    "plt.show()"
   ],
   "id": "a55191d3e6b97814",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 38
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": "",
   "id": "1fc65537f7a38a49"
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
